{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "413b8f08-e1ac-4677-9799-02d78befcd91",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Imports\n",
    "import os\n",
    "from pathlib import Path\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import statsmodels.api as sm\n",
    "import statsmodels.formula.api as smf\n",
    "from scipy.stats import t as student_t\n",
    "import re\n",
    "from matplotlib import colors\n",
    "# Matplotlib defaults (no specific styles/colors per instruction)\n",
    "plt.rcParams[\"figure.figsize\"] = (6,4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "41267baf-cc18-4e07-8820-1759ae459166",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using data folder: E:\\불평등 연구\\데이터\\63_이민자_정치적허용\n"
     ]
    }
   ],
   "source": [
    "# >>> Set your local data folder here (Windows example)\n",
    "BASE_DIR = r\"E:\\\"\n",
    "\n",
    "RESP_FILE = \"survey_respondent.csv\"\n",
    "CJ_FILE   = \"conjoint_long.csv\"\n",
    "\n",
    "BASE_DIR = Path(BASE_DIR)\n",
    "RESP_PATH = BASE_DIR / RESP_FILE\n",
    "CJ_PATH   = BASE_DIR / CJ_FILE\n",
    "\n",
    "OUT_DIR = BASE_DIR / \"outputs_notebook\"\n",
    "OUT_DIR.mkdir(parents=True, exist_ok=True)\n",
    "\n",
    "print('Using data folder:', BASE_DIR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "fd03f507-2f3c-4c9b-a6c8-73f556a0f931",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>educ</th>\n",
       "      <th>income_decile</th>\n",
       "      <th>home_owner</th>\n",
       "      <th>occupation</th>\n",
       "      <th>marital_partner</th>\n",
       "      <th>religion</th>\n",
       "      <th>ideology</th>\n",
       "      <th>...</th>\n",
       "      <th>v_vote_local</th>\n",
       "      <th>v_vote_national</th>\n",
       "      <th>v_party_rights</th>\n",
       "      <th>v_run_office</th>\n",
       "      <th>PolRightIndex_full</th>\n",
       "      <th>GapIndex_full</th>\n",
       "      <th>mc_recip</th>\n",
       "      <th>mc_ident</th>\n",
       "      <th>Med_Recip</th>\n",
       "      <th>Med_Threat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>75</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>3.25</td>\n",
       "      <td>-1.50</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4.13</td>\n",
       "      <td>4.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>51</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>3.00</td>\n",
       "      <td>-3.00</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>5.12</td>\n",
       "      <td>6.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>23</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>4.50</td>\n",
       "      <td>-0.25</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>4.40</td>\n",
       "      <td>4.27</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 34 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   id  sex  age  educ  income_decile  home_owner  occupation  marital_partner  \\\n",
       "0   1    2   75     2              5           0           9                0   \n",
       "1   2    1   51     5              6           1           6                0   \n",
       "2   3    1   23     4              5           1           9                1   \n",
       "\n",
       "   religion  ideology  ...  v_vote_local  v_vote_national  v_party_rights  \\\n",
       "0         0         6  ...             5                3               3   \n",
       "1         3         6  ...             3                4               3   \n",
       "2         0         4  ...             5                5               5   \n",
       "\n",
       "   v_run_office  PolRightIndex_full  GapIndex_full  mc_recip  mc_ident  \\\n",
       "0             2                3.25          -1.50         4         4   \n",
       "1             2                3.00          -3.00         5         6   \n",
       "2             3                4.50          -0.25         4         5   \n",
       "\n",
       "   Med_Recip  Med_Threat  \n",
       "0       4.13        4.28  \n",
       "1       5.12        6.05  \n",
       "2       4.40        4.27  \n",
       "\n",
       "[3 rows x 34 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>task</th>\n",
       "      <th>side</th>\n",
       "      <th>cj_condition</th>\n",
       "      <th>cj_rights</th>\n",
       "      <th>cj_rate</th>\n",
       "      <th>cj_choose</th>\n",
       "      <th>cj_condition_label</th>\n",
       "      <th>cj_rights_label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>A</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>C1_3yrs_tax</td>\n",
       "      <td>R1_local_vote</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>B</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>C2_5yrs_tax_lang</td>\n",
       "      <td>R1_local_vote</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>A</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>C1_3yrs_tax</td>\n",
       "      <td>R3_party_donate</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id  task side  cj_condition  cj_rights  cj_rate  cj_choose  \\\n",
       "0   1     1    A             1          1        5          0   \n",
       "1   1     1    B             2          1        5          1   \n",
       "2   1     2    A             1          3        4          0   \n",
       "\n",
       "  cj_condition_label  cj_rights_label  \n",
       "0        C1_3yrs_tax    R1_local_vote  \n",
       "1   C2_5yrs_tax_lang    R1_local_vote  \n",
       "2        C1_3yrs_tax  R3_party_donate  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Resp N = 3000 | CJ rows = 30000\n"
     ]
    }
   ],
   "source": [
    "\n",
    "resp = pd.read_csv(RESP_PATH, encoding=\"utf-8-sig\")\n",
    "cj   = pd.read_csv(CJ_PATH, encoding=\"utf-8-sig\")\n",
    "\n",
    "# Ensure some integer dtypes\n",
    "for col in [\"T_recip\",\"T_ident\",\"sex\",\"home_owner\",\"marital_partner\",\"urban_3cat\"]:\n",
    "    if col in resp.columns:\n",
    "        resp[col] = resp[col].astype(int)\n",
    "\n",
    "display(resp.head(3))\n",
    "display(cj.head(3))\n",
    "print('Resp N =', len(resp), '| CJ rows =', len(cj))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5333495b-fbd3-41ef-8caf-d3f6530bff56",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "c91c68be-0751-42a2-9d1f-fd0cd12c388c",
   "metadata": {},
   "source": [
    "## 1) Baseline: Inclusion vs Political Rights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "74a105a0-b37e-412e-a50b-9cf6776dca32",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>metric</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>IncluIndex mean</td>\n",
       "      <td>5.384146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>PolRightIndex_full mean</td>\n",
       "      <td>3.759684</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>GapIndex_full mean</td>\n",
       "      <td>-1.624462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>v_vote_local mean</td>\n",
       "      <td>4.559541</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>v_vote_national mean</td>\n",
       "      <td>4.124821</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>v_party_rights mean</td>\n",
       "      <td>3.509326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>v_run_office mean</td>\n",
       "      <td>2.845050</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    metric     value\n",
       "0          IncluIndex mean  5.384146\n",
       "1  PolRightIndex_full mean  3.759684\n",
       "2       GapIndex_full mean -1.624462\n",
       "3        v_vote_local mean  4.559541\n",
       "4     v_vote_national mean  4.124821\n",
       "5      v_party_rights mean  3.509326\n",
       "6        v_run_office mean  2.845050"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Paired t-test: mean diff = -1.624, t = -50.860, p = 0.00000\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ✅ Control group\n",
    "control = resp[(resp[\"T_recip\"] == 0) & (resp[\"T_ident\"] == 0)]\n",
    "\n",
    "# ✅ 요약 함수 수정: control group만 사용\n",
    "def baseline_summary(df: pd.DataFrame) -> pd.DataFrame:\n",
    "    out = pd.DataFrame({\n",
    "        \"metric\": [\n",
    "            \"IncluIndex mean\", \"PolRightIndex_full mean\", \"GapIndex_full mean\",\n",
    "            \"v_vote_local mean\", \"v_vote_national mean\", \"v_party_rights mean\", \"v_run_office mean\"\n",
    "        ],\n",
    "        \"value\": [\n",
    "            df[\"IncluIndex\"].mean(),\n",
    "            df[\"PolRightIndex_full\"].mean(),\n",
    "            (df[\"PolRightIndex_full\"] - df[\"IncluIndex\"]).mean(),\n",
    "            df[\"v_vote_local\"].mean(),\n",
    "            df[\"v_vote_national\"].mean(),\n",
    "            df[\"v_party_rights\"].mean(),\n",
    "            df[\"v_run_office\"].mean()\n",
    "        ]\n",
    "    })\n",
    "    return out\n",
    "\n",
    "base_tbl = baseline_summary(control)\n",
    "display(base_tbl)\n",
    "\n",
    "# ✅ Paired t-test: PolRight vs Inclusion \n",
    "inclu = control[\"IncluIndex\"]\n",
    "pol   = control[\"PolRightIndex_full\"]\n",
    "diff  = pol - inclu\n",
    "t_stat = diff.mean() / (diff.std(ddof=1)/np.sqrt(len(diff)))\n",
    "p_val  = 2*(1 - student_t.cdf(np.abs(t_stat), df=len(diff)-1))\n",
    "print(f\"Paired t-test: mean diff = {diff.mean():.3f}, t = {t_stat:.3f}, p = {p_val:.5f}\")\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(6,3))\n",
    "\n",
    "means = [inclu.mean(), pol.mean()]\n",
    "stds  = [inclu.std(ddof=1), pol.std(ddof=1)]\n",
    "\n",
    "bars = ax.bar([\"Inclusion\", \"Political rights\"], means, yerr=stds,\n",
    "              capsize=8, color=[\"#4472c4\", \"#ed7d31\"], alpha=0.85, width=0.6)\n",
    "\n",
    "ax.set_ylim(0, 7)\n",
    "ax.set_ylabel(\"Mean (1–7)\", fontsize=13)\n",
    "#ax.set_title(\"Baseline (Control Group): Inclusion vs. Political Rights\", fontsize=15, pad=10)\n",
    "ax.tick_params(axis=\"x\", labelsize=14)\n",
    "ax.tick_params(axis=\"y\", labelsize=12)\n",
    "ax.grid(axis=\"y\", linestyle=\"--\", alpha=0.7)\n",
    "\n",
    "for i, (m, s) in enumerate(zip(means, stds)):\n",
    "    ax.text(i, m + s + 0.08, f\"{m:.2f} ± {s:.2f}\", \n",
    "            ha=\"center\", va=\"bottom\", fontsize=13)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig(OUT_DIR / \"fig_2.png\", dpi=600)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6c6fbe0c-a609-4936-a251-93827ede99ae",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cf5bbbec-b6d3-4581-b66b-8ea3f70c3996",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "dd91fdab-edd9-4a59-b8d8-13474f6ff3f1",
   "metadata": {},
   "source": [
    "## 2) Gap regression: who draws the boundary?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c2478963-3dfc-4ae3-a242-fb091ec88255",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:          GapIndex_full   R-squared:                       0.113\n",
      "Model:                            OLS   Adj. R-squared:                  0.106\n",
      "Method:                 Least Squares   F-statistic:                     17.37\n",
      "Date:                Tue, 11 Nov 2025   Prob (F-statistic):           5.05e-63\n",
      "Time:                        00:30:57   Log-Likelihood:                -3807.5\n",
      "No. Observations:                3000   AIC:                             7661.\n",
      "Df Residuals:                    2977   BIC:                             7799.\n",
      "Df Model:                          22                                         \n",
      "Covariance Type:                  HC1                                         \n",
      "======================================================================================\n",
      "                         coef    std err          z      P>|z|      [0.025      0.975]\n",
      "--------------------------------------------------------------------------------------\n",
      "Intercept             -0.9747      0.183     -5.318      0.000      -1.334      -0.615\n",
      "C(occupation)[T.2]    -0.0141      0.079     -0.178      0.858      -0.170       0.141\n",
      "C(occupation)[T.3]    -0.0190      0.085     -0.224      0.823      -0.185       0.147\n",
      "C(occupation)[T.4]    -0.0393      0.087     -0.454      0.650      -0.209       0.131\n",
      "C(occupation)[T.5]    -0.0438      0.078     -0.564      0.573      -0.196       0.108\n",
      "C(occupation)[T.6]    -0.1063      0.104     -1.019      0.308      -0.311       0.098\n",
      "C(occupation)[T.7]    -0.0268      0.080     -0.336      0.737      -0.184       0.130\n",
      "C(occupation)[T.8]    -0.0554      0.087     -0.640      0.522      -0.225       0.114\n",
      "C(occupation)[T.9]    -0.0540      0.083     -0.649      0.516      -0.217       0.109\n",
      "C(religion)[T.1]      -0.0557      0.039     -1.429      0.153      -0.132       0.021\n",
      "C(religion)[T.2]      -0.0250      0.062     -0.404      0.686      -0.146       0.096\n",
      "C(religion)[T.3]       0.0034      0.045      0.076      0.939      -0.085       0.091\n",
      "C(religion)[T.4]       0.1538      0.079      1.938      0.053      -0.002       0.309\n",
      "sex                   -0.0128      0.032     -0.407      0.684      -0.075       0.049\n",
      "age                   -0.0015      0.001     -1.410      0.159      -0.003       0.001\n",
      "educ                  -0.0055      0.016     -0.343      0.732      -0.037       0.026\n",
      "income_decile          0.0004      0.006      0.076      0.939      -0.010       0.011\n",
      "home_owner             0.0687      0.032      2.157      0.031       0.006       0.131\n",
      "marital_partner        0.0147      0.033      0.451      0.652      -0.049       0.079\n",
      "ideology              -0.1237      0.007    -16.816      0.000      -0.138      -0.109\n",
      "urban_3cat             0.0275      0.023      1.193      0.233      -0.018       0.073\n",
      "Mod_Civic              0.1026      0.015      6.966      0.000       0.074       0.132\n",
      "Mod_Ethno             -0.0853      0.017     -4.961      0.000      -0.119      -0.052\n",
      "==============================================================================\n",
      "Omnibus:                        0.746   Durbin-Watson:                   1.982\n",
      "Prob(Omnibus):                  0.689   Jarque-Bera (JB):                0.787\n",
      "Skew:                          -0.035   Prob(JB):                        0.675\n",
      "Kurtosis:                       2.963   Cond. No.                         749.\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors are heteroscedasticity robust (HC1)\n"
     ]
    }
   ],
   "source": [
    "\n",
    "gap_formula = (\"GapIndex_full ~ sex + age + educ + income_decile + home_owner \"\n",
    "               \"+ C(occupation) + marital_partner + C(religion) + ideology + urban_3cat\"\n",
    "               \" + Mod_Civic + Mod_Ethno\")\n",
    "gap_model = smf.ols(gap_formula, data=resp).fit(cov_type=\"HC1\")\n",
    "print(gap_model.summary())\n",
    "with open(OUT_DIR / \"reg_gapindex.txt\", \"w\", encoding=\"utf-8\") as f:\n",
    "    f.write(gap_model.summary().as_text())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "b332351c-0632-48fe-85be-bdc24795bcd3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:          GapIndex_full   R-squared:                       0.113\n",
      "Model:                            OLS   Adj. R-squared:                  0.106\n",
      "Method:                 Least Squares   F-statistic:                     16.62\n",
      "Date:                Tue, 11 Nov 2025   Prob (F-statistic):           1.82e-62\n",
      "Time:                        00:33:58   Log-Likelihood:                -3806.9\n",
      "No. Observations:                3000   AIC:                             7662.\n",
      "Df Residuals:                    2976   BIC:                             7806.\n",
      "Df Model:                          23                                         \n",
      "Covariance Type:                  HC1                                         \n",
      "======================================================================================\n",
      "                         coef    std err          z      P>|z|      [0.025      0.975]\n",
      "--------------------------------------------------------------------------------------\n",
      "Intercept             -0.9558      0.180     -5.307      0.000      -1.309      -0.603\n",
      "C(occupation)[T.2]    -0.0148      0.079     -0.187      0.852      -0.170       0.140\n",
      "C(occupation)[T.3]    -0.0181      0.085     -0.214      0.830      -0.184       0.148\n",
      "C(occupation)[T.4]    -0.0373      0.087     -0.431      0.666      -0.207       0.132\n",
      "C(occupation)[T.5]    -0.0437      0.078     -0.564      0.573      -0.196       0.108\n",
      "C(occupation)[T.6]    -0.1043      0.104     -1.000      0.317      -0.309       0.100\n",
      "C(occupation)[T.7]    -0.0260      0.080     -0.325      0.745      -0.182       0.130\n",
      "C(occupation)[T.8]    -0.0533      0.086     -0.616      0.538      -0.223       0.116\n",
      "C(occupation)[T.9]    -0.0528      0.083     -0.635      0.525      -0.216       0.110\n",
      "C(religion)[T.1]      -0.0550      0.039     -1.411      0.158      -0.131       0.021\n",
      "C(religion)[T.2]      -0.0230      0.062     -0.372      0.710      -0.145       0.098\n",
      "C(religion)[T.3]       0.0038      0.045      0.085      0.932      -0.084       0.092\n",
      "C(religion)[T.4]       0.1546      0.079      1.947      0.052      -0.001       0.310\n",
      "C(urban_3cat)[T.2]     0.0528      0.034      1.557      0.119      -0.014       0.119\n",
      "C(urban_3cat)[T.3]     0.0329      0.051      0.643      0.520      -0.067       0.133\n",
      "sex                   -0.0127      0.032     -0.402      0.688      -0.075       0.049\n",
      "age                   -0.0015      0.001     -1.399      0.162      -0.003       0.001\n",
      "educ                  -0.0050      0.016     -0.315      0.753      -0.036       0.026\n",
      "income_decile          0.0006      0.006      0.104      0.917      -0.010       0.011\n",
      "home_owner             0.0691      0.032      2.169      0.030       0.007       0.131\n",
      "marital_partner        0.0138      0.033      0.422      0.673      -0.050       0.078\n",
      "ideology              -0.1237      0.007    -16.820      0.000      -0.138      -0.109\n",
      "Mod_Civic              0.1022      0.015      6.928      0.000       0.073       0.131\n",
      "Mod_Ethno             -0.0853      0.017     -4.967      0.000      -0.119      -0.052\n",
      "==============================================================================\n",
      "Omnibus:                        0.683   Durbin-Watson:                   1.983\n",
      "Prob(Omnibus):                  0.711   Jarque-Bera (JB):                0.721\n",
      "Skew:                          -0.034   Prob(JB):                        0.697\n",
      "Kurtosis:                       2.967   Cond. No.                         745.\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors are heteroscedasticity robust (HC1)\n",
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:     PolRightIndex_full   R-squared:                       0.323\n",
      "Model:                            OLS   Adj. R-squared:                  0.317\n",
      "Method:                 Least Squares   F-statistic:                     60.62\n",
      "Date:                Tue, 11 Nov 2025   Prob (F-statistic):          5.51e-228\n",
      "Time:                        00:33:58   Log-Likelihood:                -3221.5\n",
      "No. Observations:                3000   AIC:                             6491.\n",
      "Df Residuals:                    2976   BIC:                             6635.\n",
      "Df Model:                          23                                         \n",
      "Covariance Type:                  HC1                                         \n",
      "======================================================================================\n",
      "                         coef    std err          z      P>|z|      [0.025      0.975]\n",
      "--------------------------------------------------------------------------------------\n",
      "Intercept              5.0425      0.148     34.044      0.000       4.752       5.333\n",
      "C(occupation)[T.2]    -0.0422      0.064     -0.661      0.509      -0.168       0.083\n",
      "C(occupation)[T.3]    -0.0288      0.068     -0.426      0.670      -0.162       0.104\n",
      "C(occupation)[T.4]    -0.0766      0.070     -1.099      0.272      -0.213       0.060\n",
      "C(occupation)[T.5]    -0.0460      0.063     -0.731      0.465      -0.169       0.077\n",
      "C(occupation)[T.6]    -0.0755      0.082     -0.926      0.355      -0.236       0.084\n",
      "C(occupation)[T.7]    -0.0336      0.064     -0.527      0.598      -0.159       0.092\n",
      "C(occupation)[T.8]    -0.0338      0.068     -0.497      0.619      -0.167       0.100\n",
      "C(occupation)[T.9]    -0.0555      0.067     -0.827      0.408      -0.187       0.076\n",
      "C(religion)[T.1]      -0.0121      0.032     -0.375      0.707      -0.075       0.051\n",
      "C(religion)[T.2]      -0.0120      0.052     -0.231      0.817      -0.114       0.090\n",
      "C(religion)[T.3]      -0.0066      0.037     -0.176      0.860      -0.080       0.066\n",
      "C(religion)[T.4]       0.1048      0.063      1.656      0.098      -0.019       0.229\n",
      "C(urban_3cat)[T.2]     0.0285      0.028      1.028      0.304      -0.026       0.083\n",
      "C(urban_3cat)[T.3]     0.0430      0.043      1.009      0.313      -0.041       0.127\n",
      "sex                   -0.0012      0.026     -0.045      0.964      -0.052       0.050\n",
      "age                   -0.0024      0.001     -2.756      0.006      -0.004      -0.001\n",
      "educ                   0.0117      0.013      0.897      0.370      -0.014       0.037\n",
      "income_decile         -0.0022      0.005     -0.485      0.628      -0.011       0.007\n",
      "home_owner             0.0497      0.026      1.886      0.059      -0.002       0.101\n",
      "marital_partner        0.0125      0.027      0.464      0.643      -0.040       0.065\n",
      "ideology              -0.1797      0.006    -28.890      0.000      -0.192      -0.167\n",
      "Mod_Civic              0.1941      0.012     15.759      0.000       0.170       0.218\n",
      "Mod_Ethno             -0.2239      0.014    -15.917      0.000      -0.251      -0.196\n",
      "==============================================================================\n",
      "Omnibus:                        1.403   Durbin-Watson:                   1.974\n",
      "Prob(Omnibus):                  0.496   Jarque-Bera (JB):                1.445\n",
      "Skew:                          -0.035   Prob(JB):                        0.486\n",
      "Kurtosis:                       2.919   Cond. No.                         745.\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors are heteroscedasticity robust (HC1)\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1150x460 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "gap_formula = (\n",
    "    \"GapIndex_full ~ sex + age + educ + income_decile + home_owner \"\n",
    "    \"+ C(occupation) + marital_partner + C(religion) + ideology + C(urban_3cat) \"\n",
    "    \"+ Mod_Civic + Mod_Ethno\"\n",
    ")\n",
    "pol_formula = (\n",
    "    \"PolRightIndex_full ~ sex + age + educ + income_decile + home_owner \"\n",
    "    \"+ C(occupation) + marital_partner + C(religion) + ideology + C(urban_3cat) \"\n",
    "    \"+ Mod_Civic + Mod_Ethno\"\n",
    ")\n",
    "\n",
    "gap_model = smf.ols(gap_formula, data=resp).fit(cov_type=\"HC1\")\n",
    "pol_model = smf.ols(pol_formula, data=resp).fit(cov_type=\"HC1\")\n",
    "\n",
    "print(gap_model.summary())\n",
    "print(pol_model.summary())\n",
    "\n",
    "with open(OUT_DIR / \"reg_gapindex.txt\", \"w\", encoding=\"utf-8\") as f:\n",
    "    f.write(gap_model.summary().as_text())\n",
    "with open(OUT_DIR / \"reg_polrights.txt\", \"w\", encoding=\"utf-8\") as f:\n",
    "    f.write(pol_model.summary().as_text())\n",
    "\n",
    "def tidy_keep(model, keep_terms, label_map):\n",
    "    ci = model.conf_int()\n",
    "    ci.columns = [\"ci_low\", \"ci_high\"]\n",
    "    df = pd.concat([model.params.rename(\"coef\"), model.bse.rename(\"se\"), ci], axis=1)\n",
    "    df = df.loc[[t for t in df.index if t in keep_terms]].copy()\n",
    "    df[\"label\"] = df.index.map(lambda x: label_map.get(x, x))\n",
    "    df = df.reset_index().rename(columns={\"index\": \"term\"})\n",
    "    return df\n",
    "\n",
    "keep = [\"Mod_Civic\", \"Mod_Ethno\"]\n",
    "labels = {\"Mod_Civic\": \"Civic identity\", \"Mod_Ethno\": \"Ethnic identity\"}\n",
    "\n",
    "df_gap = tidy_keep(gap_model, keep, labels)\n",
    "df_pol = tidy_keep(pol_model, keep, labels)\n",
    "\n",
    "# 고정 순서\n",
    "order = [\"Mod_Civic\", \"Mod_Ethno\"]\n",
    "df_gap = df_gap.set_index(\"term\").loc[order].reset_index()\n",
    "df_pol = df_pol.set_index(\"term\").loc[order].reset_index()\n",
    "\n",
    "# === 3) (a) Gap, (b) Political rights ===\n",
    "fig, axes = plt.subplots(1, 2, figsize=(11.5, 4.6), sharey=True)\n",
    "\n",
    "for ax, dfi, title in [\n",
    "    (axes[0], df_gap, \"(a) Effects on Pol–Inclusion Gap\"),\n",
    "    (axes[1], df_pol, \"(b) Effects on Political Rights Acceptance\")\n",
    "]:\n",
    "    y = range(len(dfi))\n",
    "    xerr = [dfi[\"coef\"] - dfi[\"ci_low\"], dfi[\"ci_high\"] - dfi[\"coef\"]]\n",
    "    ax.errorbar(dfi[\"coef\"], y, xerr=xerr, fmt=\"o\", capsize=5, linewidth=1.2)\n",
    "    ax.axvline(0, linestyle=\"--\", linewidth=1, color='red')\n",
    "    ax.set_yticks(y)\n",
    "    ax.set_yticklabels(dfi[\"label\"], fontsize=13)\n",
    "    ax.set_xlabel(\"Coefficient (HC1)\", fontsize=12)\n",
    "    ax.set_title(title, fontsize=16, pad=10)\n",
    "    ax.grid(axis=\"x\", linestyle=\"--\", alpha=0.65)\n",
    "    ax.tick_params(axis=\"x\", labelsize=12)\n",
    "\n",
    "    \n",
    "    for i, c in enumerate(dfi[\"coef\"]):\n",
    "        ax.text(c, i - 0.02, f\"{c:.2f}\", ha=\"center\", va=\"top\", fontsize=12)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig(OUT_DIR / \"fig_3.png\", dpi=600)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a6a41aac-6a69-4820-9b5a-9e4ab08cc70b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "452e5fe4-bcf9-404a-a215-2e25d7352fde",
   "metadata": {},
   "source": [
    "## 3) Vignette 2×2 factorial effects"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "a786e657-5bfd-4be2-b1b4-163472683e93",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:     PolRightIndex_full   R-squared:                       0.418\n",
      "Model:                            OLS   Adj. R-squared:                  0.413\n",
      "Method:                 Least Squares   F-statistic:                     87.47\n",
      "Date:                Tue, 11 Nov 2025   Prob (F-statistic):               0.00\n",
      "Time:                        00:33:16   Log-Likelihood:                -2993.1\n",
      "No. Observations:                3000   AIC:                             6038.\n",
      "Df Residuals:                    2974   BIC:                             6194.\n",
      "Df Model:                          25                                         \n",
      "Covariance Type:                  HC1                                         \n",
      "======================================================================================\n",
      "                         coef    std err          z      P>|z|      [0.025      0.975]\n",
      "--------------------------------------------------------------------------------------\n",
      "Intercept              5.0470      0.143     35.321      0.000       4.767       5.327\n",
      "C(occupation)[T.2]    -0.0708      0.057     -1.232      0.218      -0.183       0.042\n",
      "C(occupation)[T.3]    -0.0512      0.061     -0.836      0.403      -0.171       0.069\n",
      "C(occupation)[T.4]    -0.0923      0.064     -1.450      0.147      -0.217       0.032\n",
      "C(occupation)[T.5]    -0.0601      0.057     -1.052      0.293      -0.172       0.052\n",
      "C(occupation)[T.6]    -0.0837      0.076     -1.103      0.270      -0.232       0.065\n",
      "C(occupation)[T.7]    -0.0683      0.058     -1.184      0.236      -0.181       0.045\n",
      "C(occupation)[T.8]    -0.0561      0.062     -0.908      0.364      -0.177       0.065\n",
      "C(occupation)[T.9]    -0.0769      0.061     -1.254      0.210      -0.197       0.043\n",
      "C(religion)[T.1]       0.0002      0.030      0.006      0.995      -0.058       0.058\n",
      "C(religion)[T.2]       0.0317      0.049      0.647      0.517      -0.064       0.128\n",
      "C(religion)[T.3]       0.0226      0.034      0.658      0.510      -0.045       0.090\n",
      "C(religion)[T.4]       0.0809      0.058      1.384      0.166      -0.034       0.196\n",
      "T_recip                0.3713      0.035     10.744      0.000       0.304       0.439\n",
      "T_ident               -0.3677      0.034    -10.678      0.000      -0.435      -0.300\n",
      "T_recip:T_ident        0.1176      0.048      2.443      0.015       0.023       0.212\n",
      "sex                   -0.0027      0.024     -0.112      0.911      -0.050       0.045\n",
      "age                   -0.0024      0.001     -3.046      0.002      -0.004      -0.001\n",
      "educ                   0.0068      0.012      0.570      0.568      -0.017       0.030\n",
      "income_decile         -0.0019      0.004     -0.437      0.662      -0.010       0.006\n",
      "home_owner             0.0345      0.024      1.415      0.157      -0.013       0.082\n",
      "marital_partner        0.0021      0.025      0.085      0.932      -0.047       0.051\n",
      "ideology              -0.1832      0.006    -31.751      0.000      -0.195      -0.172\n",
      "urban_3cat             0.0245      0.018      1.376      0.169      -0.010       0.059\n",
      "Mod_Civic              0.1913      0.011     16.856      0.000       0.169       0.214\n",
      "Mod_Ethno             -0.2208      0.013    -16.795      0.000      -0.247      -0.195\n",
      "==============================================================================\n",
      "Omnibus:                        1.606   Durbin-Watson:                   1.970\n",
      "Prob(Omnibus):                  0.448   Jarque-Bera (JB):                1.627\n",
      "Skew:                          -0.032   Prob(JB):                        0.443\n",
      "Kurtosis:                       2.906   Cond. No.                         755.\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors are heteroscedasticity robust (HC1)\n",
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:          GapIndex_full   R-squared:                       0.202\n",
      "Model:                            OLS   Adj. R-squared:                  0.195\n",
      "Method:                 Least Squares   F-statistic:                     31.42\n",
      "Date:                Tue, 11 Nov 2025   Prob (F-statistic):          1.57e-131\n",
      "Time:                        00:33:17   Log-Likelihood:                -3648.2\n",
      "No. Observations:                3000   AIC:                             7348.\n",
      "Df Residuals:                    2974   BIC:                             7505.\n",
      "Df Model:                          25                                         \n",
      "Covariance Type:                  HC1                                         \n",
      "======================================================================================\n",
      "                         coef    std err          z      P>|z|      [0.025      0.975]\n",
      "--------------------------------------------------------------------------------------\n",
      "Intercept             -0.9308      0.177     -5.264      0.000      -1.277      -0.584\n",
      "C(occupation)[T.2]    -0.0451      0.075     -0.603      0.546      -0.191       0.101\n",
      "C(occupation)[T.3]    -0.0429      0.080     -0.537      0.591      -0.200       0.114\n",
      "C(occupation)[T.4]    -0.0554      0.082     -0.673      0.501      -0.217       0.106\n",
      "C(occupation)[T.5]    -0.0593      0.074     -0.805      0.421      -0.204       0.085\n",
      "C(occupation)[T.6]    -0.1163      0.100     -1.162      0.245      -0.312       0.080\n",
      "C(occupation)[T.7]    -0.0628      0.076     -0.828      0.407      -0.212       0.086\n",
      "C(occupation)[T.8]    -0.0787      0.082     -0.957      0.339      -0.240       0.083\n",
      "C(occupation)[T.9]    -0.0760      0.079     -0.964      0.335      -0.230       0.078\n",
      "C(religion)[T.1]      -0.0426      0.037     -1.158      0.247      -0.115       0.030\n",
      "C(religion)[T.2]       0.0203      0.059      0.344      0.731      -0.095       0.136\n",
      "C(religion)[T.3]       0.0334      0.042      0.791      0.429      -0.049       0.116\n",
      "C(religion)[T.4]       0.1301      0.076      1.718      0.086      -0.018       0.279\n",
      "T_recip                0.3524      0.042      8.300      0.000       0.269       0.436\n",
      "T_ident               -0.4015      0.042     -9.521      0.000      -0.484      -0.319\n",
      "T_recip:T_ident        0.1694      0.060      2.836      0.005       0.052       0.286\n",
      "sex                   -0.0139      0.030     -0.464      0.643      -0.073       0.045\n",
      "age                   -0.0015      0.001     -1.551      0.121      -0.003       0.000\n",
      "educ                  -0.0104      0.015     -0.696      0.486      -0.040       0.019\n",
      "income_decile          0.0008      0.005      0.153      0.878      -0.009       0.011\n",
      "home_owner             0.0531      0.030      1.756      0.079      -0.006       0.112\n",
      "marital_partner        0.0044      0.031      0.142      0.887      -0.056       0.065\n",
      "ideology              -0.1274      0.007    -18.177      0.000      -0.141      -0.114\n",
      "urban_3cat             0.0286      0.022      1.303      0.193      -0.014       0.072\n",
      "Mod_Civic              0.0996      0.014      7.168      0.000       0.072       0.127\n",
      "Mod_Ethno             -0.0824      0.016     -5.014      0.000      -0.115      -0.050\n",
      "==============================================================================\n",
      "Omnibus:                        1.116   Durbin-Watson:                   1.992\n",
      "Prob(Omnibus):                  0.572   Jarque-Bera (JB):                1.170\n",
      "Skew:                          -0.037   Prob(JB):                        0.557\n",
      "Kurtosis:                       2.938   Cond. No.                         755.\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors are heteroscedasticity robust (HC1)\n"
     ]
    }
   ],
   "source": [
    "\n",
    "vign_formula = (\"PolRightIndex_full ~ T_recip*T_ident + sex + age +  educ + income_decile \"\n",
    "                \"+ home_owner + C(occupation) + marital_partner + C(religion) + ideology \"\n",
    "                \"+ urban_3cat + Mod_Civic + Mod_Ethno\")\n",
    "vign_model = smf.ols(vign_formula, data=resp).fit(cov_type=\"HC1\")\n",
    "print(vign_model.summary())\n",
    "with open(OUT_DIR / \"reg_vignette_polrights.txt\", \"w\", encoding=\"utf-8\") as f:\n",
    "    f.write(vign_model.summary().as_text())\n",
    "\n",
    "# Optional on Gap as outcome\n",
    "vign_gap_model = smf.ols(\"GapIndex_full ~ T_recip*T_ident + sex + age +  educ + income_decile \"\n",
    "                         \"+ home_owner + C(occupation) + marital_partner + C(religion) + ideology \"\n",
    "                         \"+ urban_3cat + Mod_Civic + Mod_Ethno\",\n",
    "                         data=resp).fit(cov_type=\"HC1\")\n",
    "print(vign_gap_model.summary())\n",
    "with open(OUT_DIR / \"reg_vignette_gap.txt\", \"w\", encoding=\"utf-8\") as f:\n",
    "    f.write(vign_gap_model.summary().as_text())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "011c25df-56fc-4a2e-81b4-ffe53e0e187f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1100x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 1) Fit full models (controls included)\n",
    "vign_formula_full_a = (\n",
    "    \"PolRightIndex_full ~ T_recip*T_ident + sex + age + educ + \"\n",
    "    \"income_decile + home_owner + C(occupation) + marital_partner + C(religion) + \"\n",
    "    \"ideology + urban_3cat + Mod_Civic + Mod_Ethno\"\n",
    ")\n",
    "vign_formula_full_b = (\n",
    "    \"GapIndex_full ~ T_recip*T_ident + sex + age + educ + \"\n",
    "    \"income_decile + home_owner + C(occupation) + marital_partner + C(religion) + \"\n",
    "    \"ideology + urban_3cat + Mod_Civic + Mod_Ethno\"\n",
    ")\n",
    "\n",
    "m_a = smf.ols(vign_formula_full_a, data=resp).fit(cov_type=\"HC1\")\n",
    "m_b = smf.ols(vign_formula_full_b, data=resp).fit(cov_type=\"HC1\")\n",
    "\n",
    "# 2) Tidy and FILTER to show ONLY key independents (treatments & interaction)\n",
    "def tidy_keep_terms(model, keep_terms, label_map):\n",
    "    ci = model.conf_int()\n",
    "    ci.columns = [\"ci_low\", \"ci_high\"]\n",
    "    df = pd.concat([model.params.rename(\"coef\"), model.bse.rename(\"se\"), ci], axis=1)\n",
    "    df = df.loc[[t for t in df.index if t in keep_terms]].copy()\n",
    "    df[\"label\"] = df.index.map(lambda x: label_map.get(x, x))\n",
    "    df = df.reset_index().rename(columns={\"index\":\"term\"})\n",
    "    return df\n",
    "\n",
    "keep = [\"T_recip\", \"T_ident\", \"T_recip:T_ident\"]\n",
    "labels = {\n",
    "    \"T_recip\": \"Reciprocity frame\",\n",
    "    \"T_ident\": \"Identity-threat frame\",\n",
    "    \"T_recip:T_ident\": \"Interaction (Recip × Threat)\"\n",
    "}\n",
    "\n",
    "df_a = tidy_keep_terms(m_a, keep, labels)\n",
    "df_b = tidy_keep_terms(m_b, keep, labels)\n",
    "\n",
    "# 3) 1×2 coefficient plot (with grid; show 95% CI, larger titles, pad=10) + numbers on points\n",
    "fig, axes = plt.subplots(1, 2, figsize=(11, 4), sharey=True)\n",
    "\n",
    "for ax, dfi, title in [\n",
    "    (axes[0], df_a, \"(a) Political rights acceptance\"),\n",
    "    (axes[1], df_b, \"(b) Pol–Includion gap (Pol − Inclu)\")\n",
    "]:\n",
    "    order = [\"T_recip\", \"T_ident\", \"T_recip:T_ident\"]\n",
    "    dfi = dfi.set_index(\"term\").loc[order].reset_index()\n",
    "\n",
    "    y = range(len(dfi))\n",
    "\n",
    "    xerr = [dfi[\"coef\"] - dfi[\"ci_low\"], dfi[\"ci_high\"] - dfi[\"coef\"]]\n",
    "    ax.errorbar(dfi[\"coef\"], y, xerr=xerr, fmt=\"o\", capsize=5, linewidth=1.2)\n",
    "    ax.axvline(0, linestyle=\"--\", linewidth=1, color='red')\n",
    "    ax.set_yticks(y)\n",
    "    ax.set_yticklabels(dfi[\"label\"])\n",
    "    ax.set_xlabel(\"Coefficient (HC1)\")\n",
    "\n",
    "    ax.set_title(title, fontsize=15, pad=10)\n",
    "\n",
    "    ax.grid(axis=\"x\", linestyle=\"--\", alpha=0.65)\n",
    "    ax.tick_params(axis=\"x\", labelsize=12)\n",
    "    ax.tick_params(axis=\"y\", labelsize=12)\n",
    "\n",
    "    for i, (c, lo, hi) in enumerate(zip(dfi[\"coef\"], dfi[\"ci_low\"], dfi[\"ci_high\"])):\n",
    "        ax.text(c, i - 0.05, f\"{c:.2f}\", va=\"top\", ha=\"center\", fontsize=12)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig(OUT_DIR / \"fig_4.png\", dpi=600)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ffb9774c-d99b-44b1-83bd-9c32625f0b63",
   "metadata": {},
   "source": [
    "## 3.1) Predicted effects"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "fc6c95b9-f9db-4051-b5d0-58485d9a6713",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1100x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 1) helper: get mean for numeric, mode for categorical/binary\n",
    "def _mode(s: pd.Series):\n",
    "    return s.mode(dropna=True).iloc[0]\n",
    "\n",
    "def build_baseline_row(df: pd.DataFrame) -> dict:\n",
    "    ctrls = [\"sex\",\"age\",\"educ\",\"income_decile\",\"home_owner\",\n",
    "             \"occupation\",\"marital_partner\",\"religion\",\n",
    "             \"ideology\",\"urban_3cat\",\"Mod_Civic\",\"Mod_Ethno\"]\n",
    "    base = {}\n",
    "    for v in ctrls:\n",
    "        if df[v].dtype.kind in \"iu\":            # int/bool-like\n",
    "            base[v] = int(_mode(df[v]))\n",
    "        elif df[v].dtype.kind in \"f\":           # float\n",
    "            base[v] = float(df[v].mean())\n",
    "        else:\n",
    "            try:\n",
    "                base[v] = int(_mode(df[v]))\n",
    "            except:\n",
    "                base[v] = _mode(df[v])\n",
    "    return base\n",
    "\n",
    "baseline = build_baseline_row(resp)\n",
    "\n",
    "# 2) treatment grid (Recip 0/1 × Threat 0/1), controls = baseline\n",
    "pred_df = pd.DataFrame(\n",
    "    [{\"T_recip\": r, \"T_ident\": t, **baseline} for r in (0,1) for t in (0,1)]\n",
    ")\n",
    "\n",
    "# 3) get predictions (+ 95% CI) from full models m_a, m_b\n",
    "pred_a = m_a.get_prediction(pred_df).summary_frame(alpha=0.05)\n",
    "pred_b = m_b.get_prediction(pred_df).summary_frame(alpha=0.05)\n",
    "\n",
    "# 조합 라벨\n",
    "labels = pd.Series([\"No frame\",\"Recip only\",\"Threat only\",\"Both frames\"])\n",
    "\n",
    "viz_a = pd.DataFrame({\n",
    "    \"label\": labels,\n",
    "    \"mean\": pred_a[\"mean\"].values,\n",
    "    \"lo\":   pred_a[\"mean_ci_lower\"].values,\n",
    "    \"hi\":   pred_a[\"mean_ci_upper\"].values\n",
    "})\n",
    "viz_b = pd.DataFrame({\n",
    "    \"label\": labels,\n",
    "    \"mean\": pred_b[\"mean\"].values,\n",
    "    \"lo\":   pred_b[\"mean_ci_lower\"].values,\n",
    "    \"hi\":   pred_b[\"mean_ci_upper\"].values\n",
    "})\n",
    "\n",
    "# 4) 1×2 (a)/(b)\n",
    "fig, axes = plt.subplots(1, 2, figsize=(11, 4), sharey=False)\n",
    "\n",
    "def plot_panel_noline(ax, df, title, ylim=None, ylabel=\"Predicted mean (1–7)\"):\n",
    "    x = np.arange(len(df))\n",
    "    yerr = [df[\"mean\"] - df[\"lo\"], df[\"hi\"] - df[\"mean\"]]\n",
    "    # fmt=\"o\" : 점만 표시 (선 없음)\n",
    "    ax.errorbar(\n",
    "        x, df[\"mean\"], yerr=yerr,\n",
    "        fmt=\"o\", markersize=8, capsize=5,\n",
    "        elinewidth=1.3, color=\"#1f77b4\"\n",
    "    )\n",
    "\n",
    "    for i, m in enumerate(df[\"mean\"]):\n",
    "        ax.text(i, m - 0.12, f\"{m:.2f}\", ha=\"center\", va=\"top\", fontsize=12)\n",
    "\n",
    "    ax.set_xticks(x)\n",
    "    ax.set_xticklabels(df[\"label\"], rotation=0, ha=\"center\", fontsize=13)\n",
    "\n",
    "    if ylim is not None:\n",
    "        ax.set_ylim(*ylim)\n",
    "    ax.grid(axis=\"y\", linestyle=\"--\", alpha=0.7)\n",
    "    ax.set_ylabel(ylabel, fontsize=14)\n",
    "    ax.tick_params(axis=\"y\", labelsize=13)\n",
    "    ax.set_title(title, fontsize=15, pad=10)\n",
    "\n",
    "# (a) \n",
    "plot_panel_noline(\n",
    "    axes[0], viz_a,\n",
    "    \"(a) Predicted political rights acceptance\",\n",
    "    ylim=(0, 7)\n",
    ")\n",
    "\n",
    "# (b) \n",
    "plot_panel_noline(\n",
    "    axes[1], viz_b,\n",
    "    \"(b) Predicted Poli–Inclusion gap\",\n",
    "    ylabel=\"Predicted gap\"\n",
    ")\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig(OUT_DIR / \"fig_4.png\", dpi=600)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "36e0ee1e-3053-48ef-b14c-e5a5c6e621c9",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "d4ac1e8d-546b-4ed0-932c-74333747b621",
   "metadata": {},
   "source": [
    "## 4) Conjoint: AMCE (rating) + interaction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "8aba6a6c-c251-485c-8c5c-e6f4d8a63bd1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                cj_rate   R-squared:                       0.389\n",
      "Model:                            OLS   Adj. R-squared:                  0.389\n",
      "Method:                 Least Squares   F-statistic:                     2107.\n",
      "Date:                Tue, 11 Nov 2025   Prob (F-statistic):               0.00\n",
      "Time:                        00:34:29   Log-Likelihood:                -35019.\n",
      "No. Observations:               30000   AIC:                         7.006e+04\n",
      "Df Residuals:                   29990   BIC:                         7.014e+04\n",
      "Df Model:                           9                                         \n",
      "Covariance Type:              cluster                                         \n",
      "========================================================================================\n",
      "                           coef    std err          z      P>|z|      [0.025      0.975]\n",
      "----------------------------------------------------------------------------------------\n",
      "Intercept                4.5136      0.014    311.811      0.000       4.485       4.542\n",
      "C(cj_condition)[T.2]     0.4619      0.011     42.335      0.000       0.440       0.483\n",
      "C(cj_condition)[T.3]     0.9167      0.011     83.433      0.000       0.895       0.938\n",
      "C(cj_rights)[T.2]       -0.4550      0.013    -35.802      0.000      -0.480      -0.430\n",
      "C(cj_rights)[T.3]       -0.7399      0.013    -58.637      0.000      -0.765      -0.715\n",
      "C(cj_rights)[T.4]       -1.3588      0.013   -106.134      0.000      -1.384      -1.334\n",
      "C(task)[T.2]             0.0039      0.013      0.305      0.760      -0.021       0.029\n",
      "C(task)[T.3]            -0.0161      0.013     -1.232      0.218      -0.042       0.010\n",
      "C(task)[T.4]            -0.0017      0.013     -0.127      0.899      -0.027       0.024\n",
      "C(task)[T.5]            -0.0155      0.013     -1.209      0.226      -0.041       0.010\n",
      "==============================================================================\n",
      "Omnibus:                        5.020   Durbin-Watson:                   1.721\n",
      "Prob(Omnibus):                  0.081   Jarque-Bera (JB):                4.841\n",
      "Skew:                           0.010   Prob(JB):                       0.0889\n",
      "Kurtosis:                       2.941   Cond. No.                         7.04\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors are robust to cluster correlation (cluster)\n",
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                cj_rate   R-squared:                       0.397\n",
      "Model:                            OLS   Adj. R-squared:                  0.396\n",
      "Method:                 Least Squares   F-statistic:                     1311.\n",
      "Date:                Tue, 11 Nov 2025   Prob (F-statistic):               0.00\n",
      "Time:                        00:34:29   Log-Likelihood:                -34838.\n",
      "No. Observations:               30000   AIC:                         6.971e+04\n",
      "Df Residuals:                   29984   BIC:                         6.984e+04\n",
      "Df Model:                          15                                         \n",
      "Covariance Type:              cluster                                         \n",
      "==========================================================================================================\n",
      "                                             coef    std err          z      P>|z|      [0.025      0.975]\n",
      "----------------------------------------------------------------------------------------------------------\n",
      "Intercept                                  4.6245      0.018    252.598      0.000       4.589       4.660\n",
      "C(cj_condition)[T.2]                       0.3339      0.021     15.653      0.000       0.292       0.376\n",
      "C(cj_condition)[T.3]                       0.7041      0.022     31.717      0.000       0.661       0.748\n",
      "C(cj_rights)[T.2]                         -0.4703      0.022    -21.351      0.000      -0.514      -0.427\n",
      "C(cj_rights)[T.3]                         -0.9415      0.022    -42.446      0.000      -0.985      -0.898\n",
      "C(cj_rights)[T.4]                         -1.5904      0.022    -72.540      0.000      -1.633      -1.547\n",
      "C(task)[T.2]                               0.0058      0.013      0.462      0.644      -0.019       0.030\n",
      "C(task)[T.3]                              -0.0133      0.013     -1.019      0.308      -0.039       0.012\n",
      "C(task)[T.4]                               0.0012      0.013      0.093      0.926      -0.024       0.027\n",
      "C(task)[T.5]                              -0.0108      0.013     -0.847      0.397      -0.036       0.014\n",
      "C(cj_condition)[T.2]:C(cj_rights)[T.2]     0.0378      0.031      1.237      0.216      -0.022       0.098\n",
      "C(cj_condition)[T.3]:C(cj_rights)[T.2]     0.0079      0.032      0.248      0.804      -0.054       0.070\n",
      "C(cj_condition)[T.2]:C(cj_rights)[T.3]     0.2136      0.031      6.836      0.000       0.152       0.275\n",
      "C(cj_condition)[T.3]:C(cj_rights)[T.3]     0.3936      0.031     12.555      0.000       0.332       0.455\n",
      "C(cj_condition)[T.2]:C(cj_rights)[T.4]     0.2558      0.030      8.416      0.000       0.196       0.315\n",
      "C(cj_condition)[T.3]:C(cj_rights)[T.4]     0.4424      0.031     14.169      0.000       0.381       0.504\n",
      "==============================================================================\n",
      "Omnibus:                        4.405   Durbin-Watson:                   1.720\n",
      "Prob(Omnibus):                  0.111   Jarque-Bera (JB):                4.254\n",
      "Skew:                           0.008   Prob(JB):                        0.119\n",
      "Kurtosis:                       2.944   Cond. No.                         18.9\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors are robust to cluster correlation (cluster)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cj[\"id\"] = cj[\"id\"].astype(int)\n",
    "\n",
    "# AMCE (rating) with task FE & respondent-clustered SE\n",
    "cj_formula = \"cj_rate ~ C(cj_condition) + C(cj_rights) + C(task)\"\n",
    "cj_model = smf.ols(cj_formula, data=cj).fit(cov_type=\"cluster\", cov_kwds={\"groups\": cj[\"id\"]})\n",
    "print(cj_model.summary())\n",
    "with open(OUT_DIR / \"cj_amce_rating.txt\", \"w\", encoding=\"utf-8\") as f:\n",
    "    f.write(cj_model.summary().as_text())\n",
    "\n",
    "# Interaction: condition x rights\n",
    "cj_int_formula = \"cj_rate ~ C(cj_condition)*C(cj_rights) + C(task)\"\n",
    "cj_int_model = smf.ols(cj_int_formula, data=cj).fit(cov_type=\"cluster\", cov_kwds={\"groups\": cj[\"id\"]})\n",
    "print(cj_int_model.summary())\n",
    "with open(OUT_DIR / \"cj_amce_rating_interaction.txt\", \"w\", encoding=\"utf-8\") as f:\n",
    "    f.write(cj_int_model.summary().as_text())\n",
    "\n",
    "# Rights hierarchy plot\n",
    "means_by_rights = cj.groupby(\"cj_rights\")[\"cj_rate\"].mean().reindex([1,2,3,4])\n",
    "fig, ax = plt.subplots()\n",
    "ax.plot([1,2,3,4], means_by_rights.values, marker=\"o\")\n",
    "ax.set_xticks([1,2,3,4])\n",
    "ax.set_xticklabels([\"Local\",\"National\",\"Party\",\"Candidacy\"])\n",
    "ax.set_ylim(1,7)\n",
    "ax.set_ylabel(\"Mean rating (1–7)\")\n",
    "ax.set_title(\"Conjoint: Rights hierarchy (mean ratings)\")\n",
    "for x,y in zip([1,2,3,4], means_by_rights.values):\n",
    "    ax.text(x, y+0.05, f\"{y:.2f}\", ha=\"center\", va=\"bottom\", fontsize=9)\n",
    "plt.tight_layout()\n",
    "plt.savefig(OUT_DIR / \"fig_conjoint_rights_hierarchy.png\", dpi=300)\n",
    "plt.show()\n",
    "\n",
    "# Heatmap: condition × rights\n",
    "pivot = cj.pivot_table(index=\"cj_condition\", columns=\"cj_rights\", values=\"cj_rate\", aggfunc=\"mean\").reindex(index=[1,2,3], columns=[1,2,3,4])\n",
    "fig, ax = plt.subplots(figsize=(6,4))\n",
    "im = ax.imshow(pivot.values, aspect=\"auto\")\n",
    "ax.set_xticks(range(4)); ax.set_yticks(range(3))\n",
    "ax.set_xticklabels([\"Local\",\"National\",\"Party\",\"Candidacy\"])\n",
    "ax.set_yticklabels([\"C1: 3y+tax\",\"C2: 5y+tax+lang\",\"C3: 10y+tax+vol\"])\n",
    "for i in range(3):\n",
    "    for j in range(4):\n",
    "        ax.text(j, i, f\"{pivot.values[i,j]:.2f}\", ha=\"center\", va=\"center\", color=\"w\", fontsize=9)\n",
    "ax.set_title(\"Conjoint mean rating by condition × rights\")\n",
    "fig.colorbar(im, ax=ax)\n",
    "plt.tight_layout()\n",
    "#plt.savefig(OUT_DIR / \"fig_conjoint_condition_rights_heatmap.png\", dpi=300)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2699e8cc-2f55-4fc2-8d96-36b13abb5559",
   "metadata": {},
   "source": [
    "## 4.1) regression visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "b7baf67e-5748-4bf4-a2e1-4f134cc27c82",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1100x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def tidy_amce(model):\n",
    "    \"\"\"Filter AMCE terms from cj_model and return tidy df with 95% CI.\"\"\"\n",
    "    ci = model.conf_int()\n",
    "    ci.columns = [\"ci_low\",\"ci_high\"]\n",
    "    df = pd.concat([model.params.rename(\"coef\"), model.bse.rename(\"se\"), ci], axis=1)\n",
    "    df = df.reset_index().rename(columns={\"index\":\"term\"})\n",
    "\n",
    "    # Keep only condition/rights (drop intercept & task FEs)\n",
    "    keep_mask = df[\"term\"].str.contains(r\"^C\\(cj_condition\\)\\[T\\.\") | df[\"term\"].str.contains(r\"^C\\(cj_rights\\)\\[T\\.\")\n",
    "    df = df[keep_mask].copy()\n",
    "\n",
    "    # Nice labels\n",
    "    def nice_label(s):\n",
    "        import re\n",
    "        if s.startswith(\"C(cj_rights)[T.\"):\n",
    "            lvl = re.findall(r\"\\[T\\.(\\d)\\]\", s)[0]\n",
    "            return {\"2\":\"R2 National\", \"3\":\"R3 Party\", \"4\":\"R4 Candidacy\"}[lvl]\n",
    "        if s.startswith(\"C(cj_condition)[T.\"):\n",
    "            lvl = re.findall(r\"\\[T\\.(\\d)\\]\", s)[0]\n",
    "            return {\"2\":\"C2 5y+tax+lang\", \"3\":\"C3 10y+tax+vol\"}[lvl]\n",
    "        return s\n",
    "\n",
    "    df[\"group\"] = df[\"term\"].str.contains(r\"^C\\(cj_rights\\)\\[T\\.\").map({True:\"rights\", False:\"condition\"})\n",
    "    df[\"label\"] = df[\"term\"].apply(nice_label)\n",
    "    return df\n",
    "\n",
    "amce_df = tidy_amce(cj_model)\n",
    "\n",
    "# Split to two panels\n",
    "df_rights    = amce_df[amce_df[\"group\"]==\"rights\"].copy()\n",
    "df_condition = amce_df[amce_df[\"group\"]==\"condition\"].copy()\n",
    "\n",
    "# Order\n",
    "rights_order = [\"R2 National\", \"R3 Party\", \"R4 Candidacy\"]   # R1 Local is baseline\n",
    "cond_order   = [\"C2 5y+tax+lang\", \"C3 10y+tax+vol\"]         # C1 3y+tax is baseline\n",
    "\n",
    "df_rights    = df_rights.set_index(\"label\").loc[rights_order].reset_index()\n",
    "df_condition = df_condition.set_index(\"label\").loc[cond_order].reset_index()\n",
    "\n",
    "# 1×2 coefficient plot (no connecting lines, show 95% CI, larger titles with pad=10)\n",
    "fig, axes = plt.subplots(1, 2, figsize=(11, 4), sharey=False)\n",
    "\n",
    "# Swap order: (a) Condition first, (b) Rights second\n",
    "for ax, dfi, title in [\n",
    "    (axes[0], df_condition, \"(a) AMCE for Obligation Conditions (vs. C1 3y+tax)\"),\n",
    "    (axes[1], df_rights,    \"(b) AMCE for Political-Rights Levels (vs. R1 Local)\")\n",
    "]:\n",
    "    y = range(len(dfi))\n",
    "    xerr = [dfi[\"coef\"] - dfi[\"ci_low\"], dfi[\"ci_high\"] - dfi[\"coef\"]]\n",
    "    ax.errorbar(dfi[\"coef\"], y, xerr=xerr, fmt=\"o\", capsize=5, linewidth=1.2)\n",
    "    ax.axvline(0, linestyle=\"--\", linewidth=1, color='red')\n",
    "    ax.set_yticks(y)\n",
    "    ax.set_yticklabels(dfi[\"label\"], fontsize=13)\n",
    "    ax.set_xlabel(\"AMCE (cluster-robust)\", fontsize=12)\n",
    "    ax.set_title(title, fontsize=14, pad=10)\n",
    "    ax.grid(axis=\"x\", linestyle=\"--\", alpha=0.65)\n",
    "    ax.tick_params(axis=\"x\", labelsize=12)\n",
    "\n",
    "    # 숫자 표기 (점 아래)\n",
    "    for i, c in enumerate(dfi[\"coef\"]):\n",
    "        ax.text(c, i - 0.02, f\"{c:.2f}\", ha=\"center\", va=\"top\", fontsize=12)\n",
    "\n",
    "    # 🔧 오른쪽 클리핑 방지: 축 여백\n",
    "    ax.margins(x=0.12)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig(OUT_DIR / \"fig_6.png\", dpi=600, bbox_inches=\"tight\", pad_inches=0.1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5e633eca-730c-4e72-adc1-e1e0ed489dc2",
   "metadata": {},
   "source": [
    "## 4.2) Right hierarchy and Interaction prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "3263da77-df48-4fea-8eae-5f4800f54f9a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x450 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 1. Rights hierarchy data\n",
    "means_by_rights = cj.groupby(\"cj_rights\")[\"cj_rate\"].mean().reindex([1,2,3,4])\n",
    "\n",
    "# 2. Condition × Rights pivot (이미 piv에 있음)\n",
    "# piv: 3×4 matrix (conditions × rights)\n",
    "\n",
    "# 3. Figure setup (1×2)\n",
    "fig, axes = plt.subplots(1, 2, figsize=(12, 4.5))\n",
    "\n",
    "# ------------------------------\n",
    "# (a) Rights hierarchy plot\n",
    "# ------------------------------\n",
    "ax1 = axes[0]\n",
    "ax1.plot([1,2,3,4], means_by_rights.values, marker=\"o\", linewidth=1.8, color=\"#1f77b4\")\n",
    "ax1.set_xticks([1,2,3,4])\n",
    "ax1.set_xticklabels([\"Local\", \"National\", \"Party\", \"Candidacy\"], fontsize=12)\n",
    "ax1.set_ylim(1,7)\n",
    "ax1.set_ylabel(\"Mean rating (1–7)\", fontsize=13)\n",
    "ax1.set_title(\"(a) Rights hierarchy (mean ratings)\", fontsize=15, pad=10)\n",
    "ax1.grid(axis=\"y\", linestyle=\"--\", alpha=0.7)\n",
    "\n",
    "# 점 위에 수치 표시\n",
    "for x, y in zip([1,2,3,4], means_by_rights.values):\n",
    "    ax1.text(x, y - 0.25, f\"{y:.2f}\", ha=\"center\", va=\"top\", fontsize=12)\n",
    "\n",
    "# ------------------------------\n",
    "# (b) Condition × Rights heatmap\n",
    "# ------------------------------\n",
    "ax2 = axes[1]\n",
    "im = ax2.imshow(piv.values, aspect=\"auto\", cmap=\"YlGnBu\")\n",
    "\n",
    "ax2.set_xticks(range(4))\n",
    "ax2.set_yticks(range(3))\n",
    "ax2.set_xticklabels([\"Local\",\"National\",\"Party\",\"Candidacy\"], fontsize=12)\n",
    "ax2.set_yticklabels([\"C1 3y+tax\",\"C2 5y+tax+lang\",\"C3 10y+tax+vol\"], fontsize=12)\n",
    "\n",
    "# 색상 대비 기준 계산\n",
    "norm = colors.Normalize(vmin=piv.values.min(), vmax=piv.values.max())\n",
    "threshold = (piv.values.max() + piv.values.min()) / 2\n",
    "\n",
    "# 셀 중앙에 값 표시 (밝기 기준에 따라 글자색 변경)\n",
    "for i in range(3):\n",
    "    for j in range(4):\n",
    "        val = piv.values[i, j]\n",
    "        text_color = \"black\" if val < threshold else \"white\"\n",
    "        ax2.text(j, i, f\"{val:.2f}\", ha=\"center\", va=\"center\",\n",
    "                 color=text_color, fontsize=13)\n",
    "\n",
    "ax2.set_title(\"(b) Predicted mean rating by Condition × Rights\",\n",
    "              fontsize=15, pad=10)\n",
    "\n",
    "# 컬러바 추가\n",
    "cbar = fig.colorbar(im, ax=ax2)\n",
    "cbar.ax.tick_params(labelsize=11)\n",
    "\n",
    "# ------------------------------\n",
    "# Layout & Export\n",
    "# ------------------------------\n",
    "plt.tight_layout()\n",
    "plt.savefig(OUT_DIR / \"fig_6.png\", dpi=300)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d5386a9c-b29d-4e46-953b-10cec0f29fc1",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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